Renewable-to-Vehicle Smart Charging: Planning, Modeling, and Load Forecasting for Grid Integration offering a multidisciplinary approach to integrating electric vehicle charging needs into renewable-powered grids.
This book begins with an overview of fundamental technical challenges, tariff mechanisms, and successful implementation examples for renewable-to-vehicle smart charging. Subsequent chapters detail cutting-edge machine learning and Internet-of-Things techniques for load-forecasting and charging resilience, before guiding readers through planning considerations including energy trading methods, fuel-cell vehicles, charging points size and location, and uncertainty modeling.
Combining a step-by-step grounding in key challenges and opportunities with practical case studies in the latest ML and AI technologies, Renewable-to-Vehicle Smart Charging: Planning, Modeling, and Load Forecasting for Grid Integration will support students, researchers, and engineers in integrating electric vehicle charging to the renewable-based grids of the future.
1. 4.1 Introduction - p. 91 2. 4.2 DT in R2V smart charging - p. 95 3. 4.3 ML for EV load forecasting - p. 99 4. 4.4 Forecasting model performance in real-world EV charging scenarios - p. 128 5. 4.5 Discussion and future directions - p. 151 6. 4.6 Summary and conclusion - p. 153 7. Chapter Five. IoT-enabled real-time load forecasting for renewable-to-vehicle smart charging - p. 161 8. Chapter Six. Renewable-to-vehicle (R2V) smart charging for enhanced charging resiliency across operational conditions - p. 189 9. Chapter Seven. A survey on energy trading mechanisms and electric vehicles market interactions for renewable-to-vehicle (R2V) smart charging ecosystem - p. 245 10. Chapter Eight. The application of fuel cell vehicles in hydrogen-based renewable systems - p. 317 11. Chapter Nine. Reliability-aware reinforcement learning for renewable-to-vehicle smart charging - p. 353 12. Chapter Ten. Renewable-based virtual power plant-enabled smart charging of electric vehicles for greenhouse gas emissions reduction in the distribution network - p. 415 13. Chapter Eleven. Smart energy management systems for supporting zero-emission electric vehicles: Challenges and future prospects in renewable energy integration - p. 445 14. Chapter Twelve. Wind-to-vehicle (W2V): Unlocking the synergy between wind energy and EV flexibility - p. 473 15. Chapter Thirteen. A comprehensive review of algorithms for optimizing the location and sizing of charging stations for renewable-to-vehicle (R2V) charging - p. 511 16. Chapter Fourteen. Self-sufficient smart hangar with mobile vertical metallic park for EVs: A 3D approach to urban charging and renewable energy integration - p. 569 17. Chapter Fifteen. The effect of renewable-to-vehicle (R2V) smart charging on greenhouse gas emission reduction - p. 641 18. Index - p. 669
Renewable-to-Vehicle Smart Charging: Planning, Modeling, and Load Forecasting for Grid Integration offering a multidisciplinary approach to integrating electric vehicle charging needs into renewable-powered grids.
This book begins with an overview of fundamental technical challenges, tariff mechanisms, and successful implementation examples for renewable-to-vehicle smart charging. Subsequent chapters detail cutting-edge machine learning and Internet-of-Things techniques for load-forecasting and charging resilience, before guiding readers through planning considerations including energy trading methods, fuel-cell vehicles, charging points size and location, and uncertainty modeling.
Combining a step-by-step grounding in key challenges and opportunities with practical case studies in the latest ML and AI technologies, Renewable-to-Vehicle Smart Charging: Planning, Modeling, and Load Forecasting for Grid Integration will support students, researchers, and engineers in integrating electric vehicle charging to the renewable-based grids of the future.
1. 4.1 Introduction - p. 91 2. 4.2 DT in R2V smart charging - p. 95 3. 4.3 ML for EV load forecasting - p. 99 4. 4.4 Forecasting model performance in real-world EV charging scenarios - p. 128 5. 4.5 Discussion and future directions - p. 151 6. 4.6 Summary and conclusion - p. 153 7. Chapter Five. IoT-enabled real-time load forecasting for renewable-to-vehicle smart charging - p. 161 8. Chapter Six. Renewable-to-vehicle (R2V) smart charging for enhanced charging resiliency across operational conditions - p. 189 9. Chapter Seven. A survey on energy trading mechanisms and electric vehicles market interactions for renewable-to-vehicle (R2V) smart charging ecosystem - p. 245 10. Chapter Eight. The application of fuel cell vehicles in hydrogen-based renewable systems - p. 317 11. Chapter Nine. Reliability-aware reinforcement learning for renewable-to-vehicle smart charging - p. 353 12. Chapter Ten. Renewable-based virtual power plant-enabled smart charging of electric vehicles for greenhouse gas emissions reduction in the distribution network - p. 415 13. Chapter Eleven. Smart energy management systems for supporting zero-emission electric vehicles: Challenges and future prospects in renewable energy integration - p. 445 14. Chapter Twelve. Wind-to-vehicle (W2V): Unlocking the synergy between wind energy and EV flexibility - p. 473 15. Chapter Thirteen. A comprehensive review of algorithms for optimizing the location and sizing of charging stations for renewable-to-vehicle (R2V) charging - p. 511 16. Chapter Fourteen. Self-sufficient smart hangar with mobile vertical metallic park for EVs: A 3D approach to urban charging and renewable energy integration - p. 569 17. Chapter Fifteen. The effect of renewable-to-vehicle (R2V) smart charging on greenhouse gas emission reduction - p. 641 18. Index - p. 669
